Research On Modeling And Controller Design Of Magnetorheological Damper

In this paper,the magnetorheological damper is taken as the research object,and the hysteresis nonlinear phenomenon of the MR damper is modeled.The variable gain fuzzy controller and the neural network PID controller are designed to reduce vibration based on the vehicle suspension system.The main research of this paper includes the following points:1)A dynamic hysteresis parametric model describing the force-velocity characteristics is proposed.Since the output damping force of the magnetorheological damper is related to the input speed and current.The expression of the third-order polynomial is used to embed the current into the model parameters.Aiming at the shortcomings of traditional particle swarm optimization(PSO),the improved particle swarm optimization(MPSO)algorithm is used to identify the parameters,which solves the shortcomings of traditional particle swarm optimization which is easy to fall into local optimum and slow convergence.2)A neural network hysteresis model based on MSDH is proposed.The MSDH operator was modeled by neural network,and the validity of the model is verified by comparing different inputs.The relative error of the nonparametric model is smaller because it does not need to identify parameters.3)Establish a current-dependent magnetorheological damper model and design a variable gain fuzzy controller.Firstly,a dynamic hysteresis unit is proposed to describe the hysteresis characteristics of the MR damper,and the current is embedded in the model parameters to establish a current-dependent magnetorheological damper model.Then,a variable gain fuzzy controller is designed for the two-degree-of-freedom 1/4 vehicle suspension system with the magnetorheological damper as the semi-active control element.The gain is changed to adapt to the random excitation of the road surface.The simulation results show that the variable gain fuzzy controller can effectively realize the vibration reduction control for the semi-active control suspension based on magnetorheological damper.Matlab simulation results show that the variable gain fuzzy controller can effectively reduce the vibration of the semi-active control suspension based on magnetorheological damper.4)Neural network PID controller designed for 1/4 vehicle suspension model.The vehicle suspension is a two-degree-of-freedom suspension composed of a magnetorheological damper and a spring.The neural network is used to adjust the PID parameters.It is modeled in simulink,where the neural network part uses the S function and the rest is built with block diagrams.The results show that the neural network PID controller semi-active suspension has a better damping effect compared with the passive suspension.